Tolerance to achieve. The algorithm terminates when either the relative
or the absolute residual is below tol.

restart : int, optional

Number of iterations between restarts. Larger values increase
iteration cost, but may be necessary for convergence.
Default is 20.

maxiter : int, optional

Maximum number of iterations. Iteration will stop after maxiter
steps even if the specified tolerance has not been achieved.

xtype : {‘f’,’d’,’F’,’D’}

This parameter is DEPRECATED — avoid using it.

The type of the result. If None, then it will be determined from
A.dtype.char and b. If A does not have a typecode method then it
will compute A.matvec(x0) to get a typecode. To save the extra
computation when A does not have a typecode attribute use xtype=0
for the same type as b or use xtype=’f’,’d’,’F’,or ‘D’.
This parameter has been superceeded by LinearOperator.

M : {sparse matrix, dense matrix, LinearOperator}

Inverse of the preconditioner of A. M should approximate the
inverse of A and be easy to solve for (see Notes). Effective
preconditioning dramatically improves the rate of convergence,
which implies that fewer iterations are needed to reach a given
error tolerance. By default, no preconditioner is used.

callback : function

User-supplied function to call after each iteration. It is called
as callback(rk), where rk is the current residual vector.

A preconditioner, P, is chosen such that P is close to A but easy to solve for.
The preconditioner parameter required by this routine is M=P^-1.
The inverse should preferably not be calculated explicitly. Rather, use the
following template to produce M: